Academic Journal
An efficient apriori algorithm for frequent pattern mining using mapreduce in healthcare data
| العنوان: | An efficient apriori algorithm for frequent pattern mining using mapreduce in healthcare data |
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| المؤلفون: | Sornalakshmi, M., Balamurali, S., Venkatesulu, M., Krishnan, M. Navaneetha, Ramasamy, Lakshmana Kumar, Kadry, Seifedine, Lim, Sangsoon |
| المصدر: | Bulletin of Electrical Engineering and Informatics; Vol 10, No 1: February 2021; 390-403 ; 2302-9285 ; 2089-3191 ; 10.11591/eei.v10i1 |
| بيانات النشر: | Institute of Advanced Engineering and Science |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Apriori Algorithm, Big Data, Frequent Itemset mining, Hadoop mapReduce, Parallel and distributed apriori algorithm |
| الوصف: | The development for data mining technology in healthcare is growing today as knowledge and data mining are a must for the medical sector. Healthcare organizations generate and gather large quantities of daily information. Use of IT allows for the automation of data mining and information that help to provide some interesting patterns which remove manual tasks and simple data extraction from electronic records, a process of electronic data transfer which secures medical records, saves lives and cuts the cost of medical care and enables early detection of infectious diseases. In this research paper an improved Apriori algorithm names enhanced parallel and distributed apriori (EPDA) is presented for the health care industry, based on the scalable environment known as Hadoop MapReduce. The main aim of the work proposed is to reduce the huge demands for resources and to reduce overhead communication when frequent data are extracted, through split-frequent data generated locally and the early removal of unusual data. The paper shows test results, whereby the EPDA performs in terms of the time and number of rules generated with a database of healthcare and different minimum support values. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| Relation: | https://beei.org/index.php/EEI/article/view/2096/1904; https://beei.org/index.php/EEI/article/view/2096 |
| DOI: | 10.11591/eei.v10i1.2096 |
| الاتاحة: | https://beei.org/index.php/EEI/article/view/2096 https://doi.org/10.11591/eei.v10i1.2096 |
| رقم الانضمام: | edsbas.11B776E2 |
| قاعدة البيانات: | BASE |
| DOI: | 10.11591/eei.v10i1.2096 |
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